def align_tile(local_exp, slicenum, rel_conv_ints):
leseg = local_exp.split('/')
rc_exp = '/'.join(leseg[:-3]) + '/'
storm_image_scale = int(10)
acq_folder = local_exp + 'acquisition/'
analysis_folder = local_exp + 'analysis/'
ISanalysisfolder = analysis_folder + 'individual_sections/'
rawstorm = analysis_folder + 'stormpngs/'
#auto_to_image#
channels = ["750storm", "647storm", "488storm"]
output_directory = rawstorm
input_directory = analysis_folder + "bins/"
image_max = float(256)
print rel_conv_ints
rel_conv_ints = map(float, rel_conv_ints.split('_'))
print rel_conv_ints
for channel in channels:
image_base = channel
def file_compare(x, y):
x = int(re.sub(r'[^\d]', r'', x))
y = int(re.sub(r'[^\d]', r'', y))
return cmp(x, y)
if os.path.isfile(input_directory):
bin_files = [
input_directory + channel + '_' + "%03d" % int(slicenum) +
'_*alist.bin'
]
else:
bin_files = sorted(
glob.glob(input_directory + channel + '_' +
"%03d" % int(slicenum) + '_*alist.bin'),
file_compare)
manual_1st = 0
index = manual_1st
for file in bin_files:
# Sort out the file names
# index = int(file.split("_")[-2])
# out_name = output_directory + image_base + "_7%03d" % index
name = os.path.basename(file)
imgname = name[:-9]
if file == (input_directory + channel + "_0001_alist.bin"):
out_name = output_directory + imgname
else:
out_name = output_directory + imgname
print file
if os.path.getsize(file) > 100000:
# Create image
image_i3g = i3togrid.I3GData(file, scale=storm_image_scale)
if (image_i3g.getNumberMolecules() > 0):
print " -> " + out_name
if file == (input_directory + channel + "_0001_alist.bin"):
index = index
else:
index += 1
image = image_i3g.i3To2DGridAllChannelsMerged()
image = numpy.transpose(image).copy()
image = image / image_max
image[(image > 1.0)] = 1.0
if (len(sys.argv) == 6):
print " inverting image"
image = 1.0 - image
arraytoimage.singleColorImage(image,
out_name,
autoscale=False)
else:
print str(os.path.getsize(
file)) + "bin file is less than 100kb! bad molecule list"
convfolder = acq_folder
#save_all_conv#
#this is modified from Hazen's dax_to_png.py
files = glob.glob(convfolder + 'Visconv_' + "%03d" % int(slicenum) +
'_*.dax')
if len(files) > 0:
for file in files:
print "File:", os.path.basename(file)
# load dax file
dax_file = daxspereader.DaxReader(file)
image = dax_file.loadAFrame(2).astype(numpy.uint16)
image = numpy.divide(image, int(rel_conv_ints[0]))
pilimage = Image.fromarray(image, 'I;16')
pilimage = pilimage.convert('L')
pilimage = pilimage.rotate(-90)
pilimage = pilimage.transpose(Image.FLIP_LEFT_RIGHT)
# save the result
name = os.path.basename(file)
idx = name.split('_')
index = (int(idx[1]))
pilimage.save(ISanalysisfolder + "%04d" % index +
"/rawimages/488" + name[:-4] + ".tif")
#488ffc
for i in range(9):
im = Image.open(acq_folder + '488Visffc_' + str(i) + '.tif')
crop = im.crop((256, 256, 512, 512))
imnp = numpy.array(crop)
imnp = numpy.reshape(imnp, (256, 256, 1))
if i == 0:
imstack = imnp
else:
imstack = numpy.concatenate((imstack, imnp), axis=2)
avgim = numpy.average(imstack, axis=2)
pilimage = Image.fromarray(avgim)
ffc488np = ndimage.gaussian_filter(pilimage, 20)
ffc488np[ffc488np == 0] = 1
ffc488mean = numpy.mean(ffc488np)
print ffc488mean
#this is modified from Hazen's dax_to_png.py
files = glob.glob(convfolder + 'Visconv_' + "%03d" % int(slicenum) +
'_*.dax')
for file in files:
print "File:", os.path.basename(file)
# load dax file
dax_file = daxspereader.DaxReader(file)
image = dax_file.loadAFrame(1).astype(numpy.uint16)
image = numpy.divide(image, int(rel_conv_ints[1]))
pilimage = Image.fromarray(image, 'I;16')
pilimage = pilimage.convert('L')
pilimage = pilimage.rotate(-90)
pilimage = pilimage.transpose(Image.FLIP_LEFT_RIGHT)
# save the result
name = os.path.basename(file)
idx = name.split('_')
index = (int(idx[1]))
pilimage.save(ISanalysisfolder + "%04d" % index +
"/rawimages/561" + name[:-4] + ".tif")
#561ffc
for i in range(9):
im = Image.open(acq_folder + '561Visffc_' + str(i) + '.tif')
crop = im.crop((256, 0, 512, 256))
imnp = numpy.array(crop)
imnp = numpy.reshape(imnp, (256, 256, 1))
if i == 0:
imstack = imnp
else:
imstack = numpy.concatenate((imstack, imnp), axis=2)
avgim = numpy.average(imstack, axis=2)
pilimage = Image.fromarray(avgim)
ffc561np = ndimage.gaussian_filter(pilimage, 20)
ffc561np[ffc561np == 0] = 1
ffc561mean = numpy.mean(ffc561np)
print ffc561mean
#this is modified from Hazen's dax_to_png.py
files = glob.glob(convfolder + 'Visconv_' + "%03d" % int(slicenum) +
'_*.dax')
for file in files:
print "File:", os.path.basename(file)
# load dax file
dax_file = daxspereader.DaxReader(file)
image = dax_file.loadAFrame(0).astype(numpy.uint16)
image = numpy.divide(image, int(rel_conv_ints[2]))
pilimage = Image.fromarray(image, 'I;16')
pilimage = pilimage.convert('L')
pilimage = pilimage.rotate(-90)
pilimage = pilimage.transpose(Image.FLIP_LEFT_RIGHT)
# save the result
name = os.path.basename(file)
idx = name.split('_')
index = (int(idx[1]))
pilimage.save(ISanalysisfolder + "%04d" % index +
"/rawimages/647" + name[:-4] + ".tif")
#647Visffc
for i in range(9):
im = Image.open(acq_folder + '647Visffc_' + str(i) + '.tif')
crop = im.crop((0, 0, 256, 256))
imnp = numpy.array(crop)
imnp = numpy.reshape(imnp, (256, 256, 1))
if i == 0:
imstack = imnp
else:
imstack = numpy.concatenate((imstack, imnp), axis=2)
avgim = numpy.average(imstack, axis=2)
pilimage = Image.fromarray(avgim)
ffcVis647np = ndimage.gaussian_filter(pilimage, 20)
ffcVis647np[ffcVis647np == 0] = 1
ffcVis647mean = numpy.mean(ffcVis647np)
print ffcVis647mean
conv_images_per_section = len(
glob.glob(convfolder + 'Visconv_' + "%03d" % int(slicenum) +
'_*.dax'))
i = int(slicenum)
for j in range(0, conv_images_per_section):
l = "%03d" % int(slicenum)
k = str(j)
im = Image.open(
(ISanalysisfolder + "%04d" % i + "/rawimages/488Visconv_" + l +
"_" + k + "_0.tif"))
im = im.convert('L')
crop = im.crop((256, 256, 512, 512))
imnp = numpy.array(crop) * ffc488mean
corr = numpy.array(imnp / ffc488np)
pilimage = Image.fromarray(corr)
pilimage = pilimage.convert('L')
pilimage.save(ISanalysisfolder + "%04d" % i +
"/rawimages/for_matlab/488Visconv_" + "%03d" % i +
"_" + "%02d" % j + "_0.tif")
im = Image.open(
(ISanalysisfolder + "%04d" % i + "/rawimages/561Visconv_" + l +
"_" + k + "_0.tif"))
im = im.convert('L')
crop = im.crop((256, 0, 512, 256))
imnp = numpy.array(crop) * ffc561mean
corr = numpy.array(imnp / ffc561np)
pilimage = Image.fromarray(corr)
pilimage = pilimage.convert('L')
pilimage.save(ISanalysisfolder + "%04d" % i +
"/rawimages/for_matlab/561Visconv_" + "%03d" % i +
"_" + "%02d" % j + "_0.tif")
im = Image.open(
(ISanalysisfolder + "%04d" % i + "/rawimages/647Visconv_" + l +
"_" + k + "_0.tif"))
im = im.convert('L')
crop = im.crop((0, 0, 256, 256))
imnp = numpy.array(crop) * ffcVis647mean
corr = numpy.array(imnp / ffcVis647np)
pilimage = Image.fromarray(corr)
pilimage = pilimage.convert('L')
pilimage.save(ISanalysisfolder + "%04d" % i +
"/rawimages/for_matlab/647Visconv_" + "%03d" % i +
"_" + "%02d" % j + "_0.tif")
if len(files) > 0:
#this is modified from Hazen's dax_to_png.py
files = glob.glob(convfolder + 'IRconv_' + "%03d" % int(slicenum) +
'_*.dax')
for file in files:
print "File:", os.path.basename(file)
# load dax file
dax_file = daxspereader.DaxReader(file)
image = dax_file.loadAFrame(1).astype(numpy.uint16)
image = numpy.divide(image, int(rel_conv_ints[4]))
pilimage = Image.fromarray(image, 'I;16')
pilimage = pilimage.convert('L')
pilimage = pilimage.rotate(-90)
pilimage = pilimage.transpose(Image.FLIP_LEFT_RIGHT)
# save the result
name = os.path.basename(file)
idx = name.split('_')
index = (int(idx[1]))
pilimage.save(ISanalysisfolder + "%04d" % index +
"/rawimages/647" + name[:-4] + ".tif")
#647IRffc
for i in range(9):
im = Image.open(acq_folder + '647IRffc_' + str(i) + '.tif')
crop = im.crop((0, 0, 256, 256))
imnp = numpy.array(crop)
imnp = numpy.reshape(imnp, (256, 256, 1))
if i == 0:
imstack = imnp
else:
imstack = numpy.concatenate((imstack, imnp), axis=2)
avgim = numpy.average(imstack, axis=2)
pilimage = Image.fromarray(avgim)
ffcIR647np = ndimage.gaussian_filter(pilimage, 20)
ffcIR647np[ffcIR647np == 0] = 1
ffcIR647mean = numpy.mean(ffcIR647np)
print ffcIR647mean
#this is modified from Hazen's dax_to_png.py
files = glob.glob(convfolder + 'IRconv_' + "%03d" % int(slicenum) +
'_*.dax')
for file in files:
print "File:", os.path.basename(file)
# load dax file
dax_file = daxspereader.DaxReader(file)
image = dax_file.loadAFrame(0).astype(numpy.uint16)
image = numpy.divide(image, int(rel_conv_ints[3]))
pilimage = Image.fromarray(image, 'I;16')
pilimage = pilimage.convert('L')
pilimage = pilimage.rotate(-90)
pilimage = pilimage.transpose(Image.FLIP_LEFT_RIGHT)
# save the result
name = os.path.basename(file)
idx = name.split('_')
index = (int(idx[1]))
pilimage.save(ISanalysisfolder + "%04d" % index +
"/rawimages/750" + name[:-4] + ".tif")
###750ffc
for i in range(9):
im = Image.open(acq_folder + '750IRffc_' + str(i) + '.tif')
crop = im.crop((0, 256, 256, 512))
imnp = numpy.array(crop)
imnp = numpy.reshape(imnp, (256, 256, 1))
if i == 0:
imstack = imnp
else:
imstack = numpy.concatenate((imstack, imnp), axis=2)
avgim = numpy.average(imstack, axis=2)
pilimage = Image.fromarray(avgim)
ffc750np = ndimage.gaussian_filter(pilimage, sigma=20)
ffc750np[ffc750np == 0] = 1
ffc750mean = numpy.mean(ffc750np)
print ffc750mean
#apply ffc to conv images
conv_images_per_section = len(
glob.glob(convfolder + 'IRconv_' + "%03d" % int(slicenum) +
'_*.dax'))
i = int(slicenum)
for j in range(0, conv_images_per_section):
l = "%03d" % int(slicenum)
k = str(j)
im = Image.open((ISanalysisfolder + "%04d" % i +
"/rawimages/647IRconv_" + l + "_" + k + "_0.tif"))
im = im.convert('L')
crop = im.crop((0, 0, 256, 256))
imnp = numpy.array(crop) * ffcIR647mean
corr = numpy.array(imnp / ffcIR647np)
pilimage = Image.fromarray(corr)
pilimage = pilimage.convert('L')
pilimage.save(ISanalysisfolder + "%04d" % i +
"/rawimages/for_matlab/647IRconv_" + "%03d" % i +
"_" + "%02d" % j + "_0.tif")
im = Image.open((ISanalysisfolder + "%04d" % i +
"/rawimages/750IRconv_" + l + "_" + k + "_0.tif"))
im = im.convert('L')
crop = im.crop((0, 256, 256, 512))
imnp = numpy.array(crop) * ffc750mean
corr = numpy.array(imnp / ffc750np)
pilimage = Image.fromarray(corr)
pilimage = pilimage.convert('L')
pilimage.save(ISanalysisfolder + "%04d" % i +
"/rawimages/for_matlab/750IRconv_" + "%03d" % i +
"_" + "%02d" % j + "_0.tif")
for p in range(1):
l = "%03d" % int(slicenum)
ps = str(p)
k = str(j)
stormfile = (rawstorm + "488storm_" + l + "_" + k + "_" + ps +
".tif")
if os.path.isfile(stormfile):
im = Image.open((stormfile)).convert("L")
imeq = ImageOps.equalize(im)
npeq = ndimage.gaussian_filter(imeq, sigma=1)
ffc488up = ndimage.zoom(ffc488np, 10, order=1)
imffc = numpy.multiply(npeq, numpy.log10(ffc488mean))
imffc = numpy.divide(imffc, numpy.log10(ffc488up))
pilimage = Image.fromarray(imffc)
pilimage = pilimage.convert('L')
imadj = ImageOps.autocontrast(pilimage)
pilimage.save(ISanalysisfolder + "%04d" % i +
"/rawimages/for_matlab/488storm_" +
"%03d" % i + "_" + "%02d" % j + "_" + ps +
".tif")
stormfile = (rawstorm + "561storm_" + l + "_" + k + "_" + ps +
".tif")
if os.path.isfile(stormfile):
im = Image.open((stormfile)).convert("L")
imeq = ImageOps.equalize(im)
npeq = ndimage.gaussian_filter(imeq, sigma=1)
ffc561up = ndimage.zoom(ffc561np, 10, order=1)
imffc = numpy.multiply(npeq, numpy.log10(ffc561mean))
imffc = numpy.divide(imffc, numpy.log10(ffc561up))
pilimage = Image.fromarray(imffc)
pilimage = pilimage.convert('L')
imadj = ImageOps.autocontrast(pilimage)
imadj.save(ISanalysisfolder + "%04d" % i +
"/rawimages/for_matlab/561storm_" + "%03d" % i +
"_" + "%02d" % j + "_" + ps + ".tif")
stormfile = (rawstorm + "647storm_" + l + "_" + k + "_" + ps +
".tif")
if os.path.isfile(stormfile):
im = Image.open((stormfile)).convert("L")
imeq = ImageOps.equalize(im)
npeq = ndimage.gaussian_filter(imeq, sigma=1)
ffcIR647up = ndimage.zoom(ffcIR647np, 10, order=1)
imffc = numpy.multiply(npeq, numpy.log10(ffcIR647mean))
imffc = numpy.divide(imffc, numpy.log10(ffcIR647up))
pilimage = Image.fromarray(imffc)
pilimage = pilimage.convert('L')
imadj = ImageOps.autocontrast(pilimage)
imadj.save(ISanalysisfolder + "%04d" % i +
"/rawimages/for_matlab/647storm_" + "%03d" % i +
"_" + "%02d" % j + "_" + ps + ".tif")
stormfile = (rawstorm + "750storm_" + l + "_" + k + "_" + ps +
".tif")
if os.path.isfile(stormfile):
im = Image.open((stormfile)).convert("L")
imeq = ImageOps.equalize(im)
npeq = ndimage.gaussian_filter(imeq, sigma=1)
ffc750up = ndimage.zoom(ffc750np, 10, order=1)
imffc = numpy.multiply(npeq, numpy.log10(ffc750mean))
imffc = numpy.divide(imffc, numpy.log10(ffc750up))
pilimage = Image.fromarray(imffc)
pilimage = pilimage.convert('L')
imadj = ImageOps.autocontrast(pilimage)
imadj.save(ISanalysisfolder + "%04d" % i +
"/rawimages/for_matlab/750storm_" + "%03d" % i +
"_" + "%02d" % j + "_" + ps + ".tif")
return
print " mid-point:", end
grid1 = i3_grid.i3To2DGridAllChannelsMerged(fmin = 0, fmax = end)
grid2 = i3_grid.i3To2DGridAllChannelsMerged(fmin = end, fmax = max_f)
# Compute FFT
print "Calculating"
grid1_fft = numpy.fft.fftshift(numpy.fft.fft2(grid1))
grid2_fft = numpy.fft.fftshift(numpy.fft.fft2(grid2))
grid1_fft_sqr = grid1_fft * numpy.conj(grid1_fft)
grid2_fft_sqr = grid2_fft * numpy.conj(grid2_fft)
grid1_grid2 = grid1_fft * numpy.conj(grid2_fft)
if 1:
arraytoimage.singleColorImage(numpy.abs(grid1_fft), "grid1")
arraytoimage.singleColorImage(numpy.abs(grid2_fft), "grid2")
[frc, frc_counts] = frc_c.frc(grid1_fft, grid2_fft)
# Plot results
for i in range(frc.size):
if (frc_counts[i] > 0):
frc[i] = frc[i]/float(frc_counts[i])
else:
frc[i] = 0.0
xvals = numpy.arange(frc.size)
xvals = xvals/(float(grid1_fft.shape[0]) * pixel_size * (1.0/float(storm_scale)))
frc = numpy.real(frc)
if 1:
import os
import sa_library.arraytoimage as arraytoimage
import sa_library.daxwriter as daxwriter
if (len(sys.argv) != 3):
print "usage: <in_hres> <out_img>"
exit()
hres = HResFile(sys.argv[1])
image = hres.sumFrames(verbose = True)
ext = os.path.splitext(sys.argv[2])[1]
if (ext == ".png"):
arraytoimage.singleColorImage(image, sys.argv[2])
elif (ext == ".dax"):
daxwriter.singleFrameDax(sys.argv[2], image)
else:
print "unrecognized extension ", ext
#
# The MIT License
#
# Copyright (c) 2012 Zhuang Lab, Harvard University
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
if 1:
import os
import sa_library.arraytoimage as arraytoimage
import sa_library.daxwriter as daxwriter
if (len(sys.argv) != 3):
print "usage: <in_hres> <out_img>"
exit()
hres = HResFile(sys.argv[1])
image = hres.sumFrames(verbose=True)
ext = os.path.splitext(sys.argv[2])[1]
if (ext == ".png"):
arraytoimage.singleColorImage(image, sys.argv[2])
elif (ext == ".dax"):
daxwriter.singleFrameDax(sys.argv[2], image)
else:
print "unrecognized extension ", ext
#
# The MIT License
#
# Copyright (c) 2012 Zhuang Lab, Harvard University
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
print " mid-point:", end
grid1 = i3_grid.i3To2DGridAllChannelsMerged(fmin=0, fmax=end)
grid2 = i3_grid.i3To2DGridAllChannelsMerged(fmin=end, fmax=max_f)
# Compute FFT
print "Calculating"
grid1_fft = numpy.fft.fftshift(numpy.fft.fft2(grid1))
grid2_fft = numpy.fft.fftshift(numpy.fft.fft2(grid2))
grid1_fft_sqr = grid1_fft * numpy.conj(grid1_fft)
grid2_fft_sqr = grid2_fft * numpy.conj(grid2_fft)
grid1_grid2 = grid1_fft * numpy.conj(grid2_fft)
if 1:
arraytoimage.singleColorImage(numpy.abs(grid1_fft), "grid1")
arraytoimage.singleColorImage(numpy.abs(grid2_fft), "grid2")
[frc, frc_counts] = frc_c.frc(grid1_fft, grid2_fft)
# Plot results
for i in range(frc.size):
if (frc_counts[i] > 0):
frc[i] = frc[i] / float(frc_counts[i])
else:
frc[i] = 0.0
xvals = numpy.arange(frc.size)
xvals = xvals / (float(grid1_fft.shape[0]) * pixel_size *
(1.0 / float(storm_scale)))
frc = numpy.real(frc)
